Realistic Quasi‐Biennial Oscillation Variability in Historical and Decadal Hindcast Simulations Using CMIP6 Forcing

Pohlmann, Holger; Müller, Wolfgang A.; Bittner, Matthias; Hettrich, Sebastian; Modali, Kameswarrao; Pankatz, Klaus; Marotzke, Jochem

doi:10.1029/2019gl084878

Cited by 30 publications

(27 citation statements)

References 32 publications

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“…We reconstruct the physics of the atmosphere by nudging temperature, vorticity, divergence and the logarithm of surface pressure (Pohlmann et al, 2019). The high-frequency 6h output serves as the target and is nudged into all 63 spherical harmonics.…”

Section: Reconstruction Simulationsmentioning

confidence: 99%

Trivial improvements of predictive skill due to direct reconstruction of global carbon cycle

Spring¹,

Dunkl²,

Hong-mei³

et al. 2021

Preprint

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Abstract. State-of-the-art carbon cycle prediction systems are initialized from reconstruction simulations in which state variables of the climate system are assimilated. While currently only the physical state variables are assimilated, biogeochemical state variables adjust to the state acquired through this assimilation indirectly instead of being assimilated themselves. In the absence of comprehensive biogeochemical reanalysis products, such approach is pragmatic. Here we evaluate a potential advantage of having perfect carbon cycle observational products to be used for direct carbon cycle reconstruction. Within an idealized perfect-model framework, we define 50 years of a control simulation under pre-industrial CO2 levels as our target representing observations. We nudge variables from this target onto arbitrary initial conditions 150 years later mimicking an assimilation simulation generating initial conditions for hindcast experiments of prediction systems. We investigate the tracking performance, i.e. bias, correlation and root-mean-square-error between the reconstruction and the target, when nudging an increasing set of atmospheric, oceanic and terrestrial variables with a focus on the global carbon cycle explaining variations in atmospheric CO2. We compare indirect versus direct carbon cycle reconstruction against a resampled threshold representing internal variability. Afterwards, we use these reconstructions to initialize ensembles to assess how well the target can be predicted after reconstruction. Interested in the ability to reconstruct global atmospheric CO2, we focus on the global carbon cycle reconstruction and predictive skill. We find that indirect carbon cycle reconstruction through physical fields reproduces the target variations on a global and regional scale much better than the resampled threshold. While reproducing the large scale variations, nudging introduces systematic regional biases in the physical state variables, on which biogeochemical cycles react very sensitively. Global annual surface oceanic pCO2 initial conditions are indirectly reconstructed with an anomaly correlation coefficient (ACC) of 0.8 and debiased root mean square error (RMSE) of 0.3 ppm. Direct reconstruction slightly improves initial conditions in ACC by +0.1 and debiased RMSE by −0.1 ppm. Indirect reconstruction of global terrestrial carbon cycle initial conditions for vegetation carbon pools track the target by ACC of 0.5 and debiased RMSE of 0.5 PgC. Direct reconstruction brings negligible improvements for air-land CO2 flux. Global atmospheric CO2 is indirectly tracked by ACC of 0.8 and debiased RMSE of 0.4 ppm. Direct reconstruction of the marine and terrestrial carbon cycles improves ACC by 0.1 and debiased RMSE by −0.1 ppm. We find improvements in global carbon cycle predictive skill from direct reconstruction compared to indirect reconstruction. After correcting for mean bias, indirect and direct reconstruction both predict the target similarly well and only moderately worse than perfect initialization after the first lead year. Our perfect-model study shows that indirect carbon cycle reconstruction yields satisfying initial conditions for global CO2 flux and atmospheric CO2. Direct carbon cycle reconstruction adds little improvements in the global carbon cycle, because imperfect reconstruction of the physical climate state impedes better biogeochemical reconstruction. These minor improvements in initial conditions yield little improvement in initialized perfect-model predictive skill. We label these minor improvements due to direct carbon cycle reconstruction trivial, as mean bias reduction yields similar improvements. As reconstruction biases in real-world prediction systems are even stronger, our results add confidence to the current practice of indirect reconstruction in carbon cycle prediction systems.

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Section: Reconstruction Simulationsmentioning

confidence: 99%

Trivial improvements of predictive skill due to direct reconstruction of global carbon cycle

Spring¹,

Dunkl²,

Hong-mei³

et al. 2021

Preprint

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“…The hist‐stratO3 experiment, in which MIROC6 is forced only with the historical stratospheric ozone data, reproduces the historical QBO (Figure S5). Since the CMIP6 ozone data (at least partially) includes ozone QBO signal (Pohlmann et al, 2019), it appears that diabatic heating anomalies associated with prescribed stratospheric ozone anomalies would feed back onto the dynamical QBO (e.g., Naoe et al, 2017). The details how the ozone QBO drives the dynamical QBO will be reported elsewhere.…”

Section: An Evaluation Of Miroc6 Predictionsmentioning

confidence: 99%

Seasonal to Decadal Predictions With MIROC6: Description and Basic Evaluation

Kataoka

Tatebe

Koyama

et al. 2020

J Adv Model Earth Syst

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The present paper presents results of seasonal-to-decadal climate predictions based on a coupled climate model called the Model for Interdisciplinary Research on Climate version 6 (MIROC6) contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6). MIROC6 is initialized every year for 1960-2018 by assimilating observed ocean temperature and salinity anomalies and full fields of sea ice concentration and by prescribing atmospheric initial states from reanalysis data. The impacts of updating the system on prediction skill are then evaluated by comparing hindcast experiments between the MIROC6 prediction system and a previous system based on MIROC version 5 (MIROC5). Skill of seasonal prediction is overall improved in association with representation and initialization of El Niño/Southern Oscillation (ENSO), the Quasi-Biennial Oscillation (QBO), and the Northern Hemisphere sea ice concentration in MIROC6. In particular, the QBO is skillfully predicted up to 3 years ahead with a maximum anomaly correlation exceeding r = 0.8. The prediction skill for the North Atlantic Oscillation in winter is also enhanced, but the prediction still suffers from model's inherent errors. On decadal timescales, MIROC6 has a larger fraction of areas of the globe with better surface temperature skill at all lead times than MIROC5, and it has predictive skill in the annual-mean sea surface temperature (SST) in the North Atlantic and the Pacific. In particular, MIROC6 hindcasts at 2-5 years lead time are able to capture the spatial structure of SST changes in the North Pacific and the eastern tropical Pacific associated with the 1970s regime shift better than MIROC5 hindcasts.

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“…As a complement to the assimilation run of the GCFS2.0 seasonal forecast system, we will also need the assimilation of the decadal prediction system developed in the MiKlip project (Pohlmann et al, 2019) because it extends 21 years farther into the past (autumn 1958–present). This system facilitates a slightly different initialization method compared to the seasonal prediction system.…”

Section: Datamentioning

confidence: 99%

Seasonal Forecasts of Winter Temperature Improved by Higher‐Order Modes of Mean Sea Level Pressure Variability in the North Atlantic Sector

Dalelane

Dobrynin

Fröhlich

2020

Geophysical Research Letters

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The variability of the sea level pressure in the North Atlantic sector is the most important driver of weather and climate in Europe. The main mode of this variability, the North Atlantic Oscillation (NAO), explains up to 50% of the total variance. Other modes, known as the Scandinavian index, East Atlantic, and East Atlantic/West Russian pattern, complement the variability of the sea level pressure, thereby influencing the European climate. It has been shown previously that a seasonal prediction system with enhanced winter NAO skill due to ensemble subsampling entails an improved prediction of the surface climate variables as well. Here, we show that a refined subselection procedure that accounts both for the NAO index and for the three additional modes of sea level pressure variability is able to further increase the prediction skill of wintertime mean sea level pressure, near‐surface temperature, and precipitation across Europe.

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Realistic Quasi‐Biennial Oscillation Variability in Historical and Decadal Hindcast Simulations Using CMIP6 Forcing

Cited by 30 publications

References 32 publications

Trivial improvements of predictive skill due to direct reconstruction of global carbon cycle

Trivial improvements of predictive skill due to direct reconstruction of global carbon cycle

Seasonal to Decadal Predictions With MIROC6: Description and Basic Evaluation

Seasonal Forecasts of Winter Temperature Improved by Higher‐Order Modes of Mean Sea Level Pressure Variability in the North Atlantic Sector

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